BACKGROUND: Many married women of reproductive age with husbands or partners are less cooperative in using contraceptives, often resulting in unintended pregnancies or secret contraceptive use. This private use of contraceptives causes women to suffer from side effects without proper support, and many women in Ethiopia die due to unintended pregnancies. However, the involvement of husbands or partners in the contraceptive decision-making process in Ethiopia is often neglected. There is also a lack of evidence regarding the determinants of husbands\' or partners\' decision-making power on contraceptive use in Ethiopia.
OBJECTIVE: This study aimed to assess the determinants of low involvement of husbands/partners in women\'s contraceptive use decision-making processes in Ethiopia.
METHODS: This study was based on Ethiopian Demographic and Health Survey data, collected from January 18 to June 27, 2016. Weighted samples of 3,669 reproductive-age women were included. Husbands\'/partners\' independent decision-making was measured by whether the husband/partner decided independently or alone about contraceptive use, categorized as \"Yes\" or \"No\". A multilevel logistic regression model was fitted, and ICC (Intra-class Correlation Coefficient), MOR (Median Odds Ratio), PCV (Proportion Change in Variance), and deviance were used to assess model fitness and comparison. Variables with a p-value of ≤ 0.2 in the bivariate analysis were considered candidates for multivariable multilevel analysis. An adjusted odds ratio with a 95% confidence interval was used to determine both the direction and strength of the association, and a p-value of < 0.05 was used to declare statistical significance.
RESULTS: Husbands\'/partners\' independent decision-making in women\'s contraceptive use was found to be 5.41% [4.72-6.19%]. Significant factors associated with this included: Husbands/partners aged 31-59 years (Adjusted Odds Ratio (AOR) = 1.3; Confidence Interval (CI) 2.3-5.4) and over 59 years (AOR = 2.3; CI 1.2-4.3), Educational level of husbands/partners: primary education (AOR = 3.2; CI 2.9-4.3), secondary education (AOR = 3.9; CI 2.7-4.4), and higher education (AOR = 4.3; CI 2.8-5.0), Media exposure (AOR = 4.5; CI 2.2-5.4), History of pregnancy termination (AOR = 3.3; CI 2.6-4.1), Perception that distance to health facilities is not a significant problem (AOR = 3.0; CI 1.7-4.7) and Urban residency (AOR = 3.5; CI 1.6-4.2).
CONCLUSIONS: In Ethiopia, the involvement of husbands/partners in the contraceptive use decision-making process is low. To increase their decision-making power, attention should be given to factors such as age, educational level, media exposure, history of pregnancy termination, distance to health facilities, and urban residency.

BACKGROUND: Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia.
OBJECTIVE: The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease.
METHODS: A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results. The inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full-text screening, out of which 26 eligible studies that met the selection criteria were included in the review. .
RESULTS: Primarily, the selected studies focused on gait analysis (n=24), while other functional tasks, such as sit to stand (n=5) and stepping in place (n=1), were also explored. However, activities of daily living were not evaluated in any of the included studies. MMC models varied across the studies, encompassing depth cameras (n=18) versus standard video cameras (n=5) or mobile phone cameras (n=2) with postprocessing using deep learning models. However, only 6 studies conducted rigorous comparisons with established gold-standard motion capture models.
CONCLUSIONS: Despite its potential as an effective tool for analyzing movement and posture in individuals with dementia, mild cognitive impairment, and Parkinson disease, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real world.

The goal of decision-making is to select one option and disregard the others. However, deliberation can also create a memory association between the chosen and unchosen options. This study aims to investigate how choice and deliberation affect the memory of postdecision options and the underlying mechanisms. Using event-related potentials (ERPs), we examined item recognition (Experiment 1) and associative recognition (Experiment 2) following certain and uncertain decisions. In Experiment 1, items that were chosen in certain decisions were remembered better than unchosen items. There was no difference between chosen and unchosen items in uncertain decisions. Moreover, a late recollection-related LPC (a late positive component) old/new effect was larger for chosen items than unchosen items in certain decisions. The early familiarity-related FN400 and the late recollection-related LPC old/new effects were significant for chosen and unchosen items in uncertain decisions. In Experiment 2, there was no difference in performance on associative memory. A FN400 old/new effect (an index of integration) in certain or uncertain decisions was not observed. Although significant LPC old/new effects were found in both certain and uncertain decisions, no difference was found between them. These results propose that decision-making can enhance item memory performance through two distinct processes: value and elaboration. Elaboration involves focusing on the details within items rather than integrating items into a cohesive whole.

BACKGROUND: The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help patients navigate the health care system more efficiently.
OBJECTIVE: The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)-based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs.
METHODS: A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists.
RESULTS: A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport\'s disease suggestion and Ada\'s top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada\'s D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada\'s diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs.
CONCLUSIONS: To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs.
BACKGROUND: German Register of Clinical Trials DRKS00017642; https://drks.de/search/en/trial/DRKS00017642.

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Continued investment in the development and application of mathematical models of poliovirus transmission, economics, and risks leads to their use in support of polio endgame strategy development and risk management policies. This study complements an earlier review covering the period 2000-2019 and discusses the evolution of studies published since 2020 by modeling groups supported by the Global Polio Eradication Initiative (GPEI) partners and others. We systematically review modeling papers published in English in peer-reviewed journals from 2020-2024.25 that focus on poliovirus transmission and health economic analyses. In spite of the long-anticipated end of poliovirus transmission and the GPEI sunset, which would lead to the end of its support for modeling, we find that the number of modeling groups supported by GPEI partners doubled and the rate of their publications increased. Modeling continued to play a role in supporting GPEI and national/regional policies, but changes in polio eradication governance, decentralized management and decision-making, and increased heterogeneity in modeling approaches and findings decreased the overall impact of modeling results. Meanwhile, the failure of the 2016 globally coordinated cessation of type 2 oral poliovirus vaccine use for preventive immunization and the introduction of new poliovirus vaccines and formulation, increased the complexity and uncertainty of poliovirus transmission and economic models and policy recommendations during this time.

UNASSIGNED: To determine whether certain patients are vulnerable to errant triage decisions immediately after major surgery and whether there are unique sociodemographic phenotypes within overtriaged and undertriaged cohorts.
UNASSIGNED: In a fair system, overtriage of low-acuity patients to intensive care units (ICUs) and undertriage of high-acuity patients to general wards would affect all sociodemographic subgroups equally.
UNASSIGNED: This multicenter, longitudinal cohort study of hospital admissions immediately after major surgery compared hospital mortality and value of care (risk-adjusted mortality/total costs) across 4 cohorts: overtriage (N = 660), risk-matched overtriage controls admitted to general wards (N = 3077), undertriage (N = 2335), and risk-matched undertriage controls admitted to ICUs (N = 4774). K-means clustering identified sociodemographic phenotypes within overtriage and undertriage cohorts.
UNASSIGNED: Compared with controls, overtriaged admissions had a predominance of male patients (56.2% vs 43.1%, P < 0.001) and commercial insurance (6.4% vs 2.5%, P < 0.001); undertriaged admissions had a predominance of Black patients (28.4% vs 24.4%, P < 0.001) and greater socioeconomic deprivation. Overtriage was associated with increased total direct costs [$16.2K ($11.4K-$23.5K) vs $14.1K ($9.1K-$20.7K), P < 0.001] and low value of care; undertriage was associated with increased hospital mortality (1.5% vs 0.7%, P = 0.002) and hospice care (2.2% vs 0.6%, P < 0.001) and low value of care. Unique sociodemographic phenotypes within both overtriage and undertriage cohorts had similar outcomes and value of care, suggesting that triage decisions, rather than patient characteristics, drive outcomes and value of care.
UNASSIGNED: Postoperative triage decisions should ensure equality across sociodemographic groups by anchoring triage decisions to objective patient acuity assessments, circumventing cognitive shortcuts and mitigating bias.

In this paper, we introduce an analytic approach for assessing effects of multilevel interventions on disparity in health outcomes and health-related decision outcomes (i.e., a treatment decision made by a healthcare provider). We outline common challenges that are encountered in interventional health disparity research, including issues of effect scale and interpretation, choice of covariates for adjustment and its impact on effect magnitude, and the methodological challenges involved with studying decision-based outcomes. To address these challenges, we introduce total effects of interventions on disparity for the entire sample and the treated sample, and corresponding direct effects that are relevant for decision-based outcomes. We provide weighting and g-computation estimators in the presence of study attrition and sketch a simulation-based procedure for sample size determinations based on precision (e.g., confidence interval width). We validate our proposed methods through a brief simulation study and apply our approach to evaluate the RICH LIFE intervention, a multilevel healthcare intervention designed to reduce racial and ethnic disparities in hypertension control.

In the natural world, animals make decisions on an ongoing basis, continuously selecting which action to undertake next. In the lab, however, the neural bases of decision processes have mostly been studied using artificial trial structures. New experimental tools based on the genetic toolkit of model organisms now make it experimentally feasible to monitor and manipulate neural activity in small subsets of neurons during naturalistic behaviors. We thus propose a new approach to investigating decision processes, termed reverse neuroethology. In this approach, experimenters select animal models based on experimental accessibility and then utilize cutting-edge tools such as connectomes and genetically encoded reagents to analyze the flow of information through an animal\'s nervous system during naturalistic choice behaviors. We describe how the reverse neuroethology strategy has been applied to understand the neural underpinnings of innate, rapid decision making, with a focus on defensive behavioral choices in the vinegar fly Drosophila melanogaster.

BACKGROUND: Preventable harms from medications are significant threats to patient safety in community settings, especially among ambulatory older adults on multiple prescription medications. Patients may partner with primary care professionals by taking on active roles in decisions, learning the basics of medication self-management, and working with community resources.
OBJECTIVE: This study aims to assess the impact of a set of patient partnership tools that redesign primary care encounters to encourage and empower patients to make more effective use of those encounters to improve medication safety.
METHODS: The study is a nonrandomized, cross-sectional stepped wedge cluster-controlled trial with 1 private family medicine clinic and 2 public safety-net primary care clinics each composing their own cluster. There are 2 intervention sequences with 1 cluster per sequence and 1 control sequence with 1 cluster. Cross-sectional surveys will be taken immediately at the conclusion of visits to the clinics during 6 time periods of 6 weeks each, with a transition period of no data collection during intervention implementation. The number of visits to be surveyed will vary by period and cluster. We plan to recruit patients and professionals for surveys during 405 visits. In the experimental periods, visits will be conducted with two partnership tools and associated clinic process changes: (1) a 1-page visit preparation guide given to relevant patients by clinic staff before seeing the provider, with the intention to improve communication and shared decision-making, and (2) a library of short educational videos that clinic staff encourage patients to watch on medication safety. In the control periods, visits will be conducted with usual care. The primary outcome will be patients\' self-efficacy in medication use. The secondary outcomes are medication-related issues such as duplicate therapies identified by primary care providers and assessment of collaborative work during visits.
RESULTS: The study was funded in September 2019. Data collection started in April 2023 and ended in December 2023. Data was collected for 405 primary care encounters during that period. As of February 15, 2024, initial descriptive statistics were calculated. Full data analysis is expected to be completed and published in the summer of 2024.
CONCLUSIONS: This study will assess the impact of patient partnership tools and associated process changes in primary care on medication use self-efficacy and medication-related issues. The study is powered to identify types of patients who may benefit most from patient engagement tools in primary care visits.
BACKGROUND: ClinicalTrials.gov NCT05880368; https://clinicaltrials.gov/study/NCT05880368.
UNASSIGNED: DERR1-10.2196/57878.